Measurement of the B 0s → ϕϕ branching fraction and search for the decay B 0 → ϕϕ

JHEP10(2015)053 Published for SISSA by Springer Received:August 5, 2015 Accepted:September 16, 2015 Published:October 8, 2015 Measurement of the B0 s→φφ branching fraction and search for the decay B0→φφ The LHCb collaboration E-mail: [email protected] Abstract: Using a dataset corresponding to an integrated luminosity of 3.0 fb−1collected in pp collisions at centre-of-mass energies of 7 and 8 TeV, the B0 s→φφ branching fraction is measured to be B(B0 s→φφ) = (1.84 ±0.05(stat) ±0.07(syst) ±0.11 (fs/fd)±0.12 (norm) ) ×10−5, where fs/fdrepresents the ratio of the B0 sto B0production cross-sections, and the B0→φK∗(892)0decay mode is used for normalization. This is the most precise measurement of this branching fraction to date, representing a factor five reduction in the statistical uncertainty compared with the previous best measurement. A search for the decay B0→φφ is also made. No signal is observed, and an upper limit on the branching fraction is set as B(B0→φφ)<2.8×10−8 at 90% confidence level. This is a factor of seven improvement compared to the previous best limit. Keywords: Hadron-Hadron Scattering, Branching fraction, B physics, Flavor physics ArXiv ePrint: 1508.00788 Open Access, Copyright CERN, for the benefit of the LHCb Collaboration. Article funded by SCOAP3. doi:10.1007/JHEP10(2015)053 JHEP10(2015)053 Contents 1 Introduction 1 2 Detector and software 2 3 Signal selection 3 4 Fits to mass spectra 4 5 Branching fraction for B0 s→φφ 5 6 Search for the decay B0→φφ 8 7 Summary 9 The LHCb collaboration 13 1 Introduction In the Standard Model, the flavour-changing neutral current decay B0 s→φφ proceeds via a¯ b→¯ss¯spenguin amplitude. The decay was first observed by the CDF experiment at the Tevatron [1]. Subsequently, it has been studied by the CDF and LHCb collaborations, who searched for CP-violating asymmetries in the decay time and angular distributions of this mode [2–5]. These studies provide a probe for possible new physics contributions entering into the penguin loop and B0 s−B0 smixing diagrams [6]. Furthermore, as the B0 s→φφ mode will be used as normalization for studies of other charmless B0 smeson decays, it is important to have a precise determination of its branching fraction. The CDF collaboration measured this relative to the decay B0 s→J/ψφ [2]. Using the current value of the B0 s→J/ψφ branching fraction [7], the CDF result gives B(B0 s→φφ) = (1.91±0.26±0.16)×10−5, where the first uncertainty is from the measured ratio to B0 s→J/ψφ, and the second is due to the knowledge of the B0 s→J/ψφ branching fraction. Various predictions from theories based on QCD factorization exist for the B0 s→φφ branching fraction [8–10]. These suffer from uncertainties related to weak annihilation diagrams. These uncertainties are controlled using experimental information from decays such as B0→φK∗(892)0. Several recent predictions are summarized in table 1. The central values are in the range (1.5−2.0)×10−5. In this paper the B0 s→φφ branching fraction (the use of charge-conjugate modes is implied throughout) is measured using the full LHCb Run 1 dataset, comprising data corresponding to an integrated luminosity of 1.0 fb−1collected in pp collisions at a centreof-mass energy of 7 TeV, and 2.0 fb−1collected at 8 TeV. The decay B0→φK∗(892)0, which has a similar topology, is used for normalization. The φand K∗(892)0mesons are – 1 – JHEP10(2015)053 B(B0 s→φφ) (10−5) Approach Reference 1.95 ±0.10+1.30 −0.80 QCD factorization [8] 1.67+0.26 −0.21+1.13 −0.88 QCD factorization [9] 1.55+2.24 −1.55 QCD factorization [10] 1.67+0.89 −0.71 pQCD [11] Table 1. Predictions for the B0 s→φφ branching fraction. The first and second uncertainties of refs. [8,9] reflect the knowledge of CKM parameters and power corrections, respectively. reconstructed in the K+K−and K+π−final states, respectively. In addition, a search for the yet unobserved decay B0→φφ is made. This decay is suppressed in the Standard Model by the OZI rule [12–14], with an expected branching fraction in the range (0.1− 3.0) ×10−8[8,10,15,16]. However, the branching fraction can be enhanced, up to the 10−7level, in models such as supersymmetry with R-parity violation [16]. The current best limit for this mode is from the BaBar collaboration [17], B(B0→φφ)<2.0×10−7at 90 % confidence level. 2 Detector and software The LHCb detector [18,19] is a single-arm forward spectrometer covering the pseudorapidity range 2 < η < 5, designed for the study of particles containing bor c quarks. The detector includes a high-precision tracking system consisting of a silicon-strip vertex detector surrounding the pp interaction region [20], a large-area silicon-strip detector located upstream of a dipole magnet with a bending power of about 4 Tm, and three stations of silicon-strip detectors and straw drift tubes [21] placed downstream of the magnet. The tracking system provides a measurement of momentum, p, with a relative uncertainty that varies from 0.5 % at low momentum to 1.0 % at 200 GeV/c. The minimum distance of a track to a primary pp interaction vertex (PV), the impact parameter, is measured with a resolution of (15 + 29/pT)µm, where pTis the component of the momentum transverse to the beam, in GeV/c. Different types of charged hadrons are distinguished using information from two ringimaging Cherenkov detectors [22]. Photon, electron and hadron candidates are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers [23]. The trigger [24] consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which applies a full event reconstruction. The software trigger applied in this analysis requires a two-, threeor four-track secondary vertex with a significant displacement from any PV. At least one charged particle must have a transverse momentum pT>1.7 GeV/c and be inconsistent with originating from – 2 – JHEP10(2015)053 a PV. A multivariate algorithm [25] is used for the identification of secondary vertices consistent with the decay of a bhadron. In the simulation, pp collisions are generated using Pythia [26,27] with a specific LHCb configuration [28]. Decays of hadronic particles are described by EvtGen [29], in which final-state radiation is generated using Photos [30]. The interaction of the generated particles with the detector, and its response, are implemented using the Geant4 toolkit [31,32] as described in ref. [33]. 3 Signal selection The selection of candidates takes place in two stages. First, a selection using loose criteria is performed that reduces background whilst retaining high signal efficiency. Following this, a multivariate method is used to further improve the signal significance. The selection starts from charged particle tracks that traverse the entire spectrometer. Selected particles are required to have pT>500 MeV/c. Fake tracks created by the reconstruction due to random combinations of hits in the detector are suppressed using a requirement on a neural network trained to discriminate between these and genuine tracks associated to particles. Combinatorial background from hadrons originating at the primary vertex is suppressed by requiring that all tracks are significantly displaced from any primary vertex. Kaon and pion candidates are selected using the information provided by the ring-imaging Cherenkov detectors. This is combined with kinematic information using a neural network to provide an effective probability that a particle is a kaon (PK) or pion (Pπ). To select kaon candidates it is required that PK(1 − Pπ)>0.025. The pion candidate in the B0→φK∗(892)0decay mode is required to have Pπ>0.2 and PK<0.2. The selected charged particles are combined to form φand K∗meson candidates. The invariant mass of the K+K−(K+π−) pair is required to be within 15 MeV/c2(150 MeV/c2) of the known mass of the φ(K∗(892)0) meson [7]. In addition, the pTof the φand K∗ mesons must be greater than 1 GeV/c. Candidates for the decay B0 s→φφ are formed by combining pairs of φmesons. A fit is made requiring all four final-state particles to originate from a common vertex, and the direction vector between the primary and secondary vertices is required to be consistent with the direction of the momentum vector of the B0 smeson candidate. Further requirements are then applied to remove background from specific b-hadron decays that peak close to the B0 smass. To reject background from B0→φK∗(892)0decays, the kaon with the lowest value of PKis considered to be a pion, and the K+π−and K+K−K+π−invariant masses are calculated. Candidates with m(K+π−) within 50 MeV/c2of the known K∗(892)0mass and m(K+K−K+π−) within 30 MeV/c2of the B0mass [7] are rejected. Similarly, to remove decays via open charm mesons, the K+K−π+mass is calculated. If m(K+K−π+) is within 22.5 MeV/c2of the D+or D+ smass [7], the candidate is rejected. These vetoes are found to retain 91% of simulated B0 s→φφ decays. Candidates for the decay B0→φK∗(892)0are formed from combinations of φand K∗ mesons. Identical vertex and pointing requirements as for the B0 s→φφ decay mode are applied. To reject background from B0 s→φφ, the mass of the K+π−pair is calculated as- – 3 – JHEP10(2015)053 suming that both hadrons are kaons. Candidates with m(K+K−) within 15 MeV/c2of the φ mass and m(K+K−K+K−) within 30 MeV/c2of the B0 smass [7] are rejected. Background from open charm decays is suppressed in a manner similar to that used for the B0 s→φφ candidates. These vetoes are found to retain 97% of simulated B0→φK∗(892)0decays. The combinatorial background is further suppressed using a Boosted Decision Tree method (BDT) [34,35]. The BDT is trained to identify four-body hadronic b-hadron decays with high efficiency using independent data samples of such decays. It uses information on the displacement of the b-hadron candidate from the primary vertex, kinematic information and track isolation criteria. Although the same BDT is used for the B0 s→φφ branching fraction measurement and the search for B0→φφ, the method used to optimize the cut on the BDT output is different. For the branching fraction measurement, the cut optimization is based on the normalisation mode B0→φK∗(892)0. The figure of merit used is S0×εS pS0×εS+Nbg , where S0is the signal yield of B0→φK∗(892)0candidates in data before any BDT cut is applied, εSis the efficiency of the BDT cut on simulated B0→φK∗(892)0decays, and Nbg is the number of background candidates surviving the BDT cut in a suitable upper sideband of the φK∗(892)0candidate mass distribution, scaled to the width of the B0signal window. Maximizing this figure of merit results in a rather loose BDT requirement that retains 98% of signal events while rejecting more than 90% of the background. For the B0→φφ search, the figure of merit used is ε0 S a/2 + qN0 bg , with aset to 3, corresponding to the signal significance required to claim evidence for a new decay mode [36]. Here ε0 Sis the efficiency of the BDT cut on simulated B0 s→φφ decays, and N0 bg is the number of background candidates surviving the BDT cut in an upper sideband of the φφ candidate mass distribution, scaled to the width of the B0 ssignal window. Maximizing this figure of merit results in a tighter BDT requirement that retains 87% of signal events. 4 Fits to mass spectra The yields for the signal and normalization channels are determined from fits to the invariant mass distributions of the selected candidates. In the simulation, the B0 s→φφ invariant mass distribution is well modelled by a probability density function (PDF) consisting of the sum of three Gaussian distributions with a common mean. In the fit to the data, the relative fractions of the Gaussian components are fixed to the values obtained from the simulation, whilst the widths are allowed to vary by an overall resolution scale factor. The yield and common mean are also left free. After applying all selection requirements, the only remaining background is combinatorial, which is modelled by a constant. No component for B0→φφ decays is included in this fit. Figure 1shows the resulting fit to data, which gives a signal yield of 2309 ±49 candidates. – 4 – JHEP10(2015)053 ] 2 c) [MeV/ − K + K − K + K(m 5200 5300 5400 5500 5600 ) 2 cCandidates / (9.0 MeV/ 1 10 2 10 LHCb Figure 1. The K+K−K+K−invariant mass distribution. The total fitted function as described in the text is shown by the (red) solid line, the B0 s→φφ component by the (blue) long-dashed line, and the combinatorial background as the (purple) dotted line. The B0→φK∗(892)0invariant mass distribution is modelled by a PDF consisting of the sum of a Crystal Ball function [37] and two Gaussian functions. As for the signal mode, the relative fractions of the components and the tail parameters are fixed in the fit to the data, whilst the widths are allowed to vary by an overall resolution scale factor. The yield and mean are also left free. A component is also included to account for the small contribution from the decay B0 s→φK∗(892)0[38]. The shape parameters for this component are shared with the B0component, while the relative position is fixed to the known mass difference between the B0and B0 smesons [7]. Combinatorial background is modelled by an exponential function. Potential peaking backgrounds, from Λ0 b→φpπ−(with the proton misidentified as a kaon) or Λ0 b→φpK−(with the proton misidentified as a pion), are modelled using a single histogram PDF generated from simulated events. The relative yield of each decay mode is weighted according to the expectation from the simulation. The yield of this component is left to float in the fit. Backgrounds from B0 s→φφ and open charm decay modes are negligible after the vetoes described in section 3have been applied. Figure 2shows the result of the fit of this model to the B0→φK∗(892)0dataset after all selection criteria are applied. The yield of B0candidates determined by the fit is 6680 ±86. 5 Branching fraction for B0 s→φφ The branching fraction of B0 s→φφ relative to that of the B0→φK∗(892)0decay mode is determined using B(B0 s→φφ) B(B0→φK∗(892)0)=Nφφ NφK∗(892)0 εsel φK∗(892)0 εsel φφ B(K∗(892)0→K+π−) B(φ→K+K−)·1 fs/fd , – 5 – JHEP10(2015)053 ] 2 c) [MeV/ − π + K − K + K(m 5200 5300 5400 5500 5600 ) 2 cCandidates / (9.0 MeV/ 1 10 2 10 3 10 LHCb Figure 2. The K+K−K+π−invariant mass distribution. The total fitted function is shown by the (red) solid line, the B0→φK∗component by the (blue) short-dashed line, the B0 s→φK∗(892)0 component by the (blue) long-dashed line, the Λ0 b→φpπ−and Λ0 b→φpK−contribution by the (green) dashed-dotted line, and the combinatoral background by the (purple) dotted line. where the terms Bare the branching fractions of the stated decay modes, Nare the signal yields, εsel are the selection efficiencies, and the fragmentation fraction ratio, fs/fd, is the ratio of the B0 sto B0production cross-sections. The selection efficiencies are determined from simulation, apart from those related to the particle identification, which are determined in data using large calibration samples of charged kaons and pions from D∗+→D0(→K−π+)π+decays [22]. The ratio of efficiencies is found to be εsel φK∗(892)0/εsel φφ = 0.795 ±0.007, where the uncertainty is purely statistical. The value of fs/fdis taken from previous LHCb analyses as 0.259 ±0.015 [39–41]. The signal yields are determined using the mass fits described in section 4. These values are corrected for the fraction of candidates where one of the hadron pairs, K+K−or K+π−, is produced in a non-resonant S-wave configuration, rather than as a φor K∗(892)0. The S-wave fractions are taken from previous LHCb angular analyses of the B0 s→φφ and B0→φK∗(892)0decay modes. For the B0 s→φφ decay mode, we use the measured value of 2.1±1.6 % [5] as the S-wave fraction within the K+K−invariant mass range used for this analysis. Similarly, for the B0→φK∗(892)0decay mode, we use a measured value of 26.5±1.8 % [42] for the S-wave fraction. The uncertainties on these fractions lead to a 3.1 % relative uncertainty on the ratio of branching fractions. This procedure assumes that the efficiencies for the Pand S-wave components are the same. In the simulation, a 1.1 % difference is observed between these efficiencies, and this is assigned as an additional uncertainty. Various other uncertainties arise on the measurement of the ratio of branching fractions. The limited size of the available simulation samples leads to a relative uncertainty of 0.8 %. The influence of the assumed mass model is probed by performing the fit with different models for the signal and background components. This includes quantifying the – 6 – JHEP10(2015)053 Source of systematic uncertainty Relative uncertainty (%) S-wave fraction 3.1 Relative efficiency between P and S-wave 1.1 Simulation sample size 0.8 Fit model 0.6 Tracking efficiency 0.5 Hadronic interactions 0.3 Hardware trigger 1.1 Particle identification efficiency 0.3 B(φ→K+K−) 1.0 Quadratic sum of the above 3.8 Fragmentation fraction ratio (fs/fd) 5.8 Table 2. Summary of the systematic uncertainties on the measurement of the ratio of branching fractions B(B0 s→φφ)/B(B0→φK∗). effect of removing the peaking background component in the B0→φK∗(892)0fit. The largest variation in the ratio of branching fractions seen in these studies is 0.6 %, which is assigned as a relative systematic uncertainty. The track reconstruction efficiency agrees between data and simulation at the level of 2.0 % [43]. This uncertainty largely cancels in the ratio of branching fractions. A residual relative uncertainty of 0.5 % remains due to the fact that the pion in the B0→φK∗(892)0 decay mode is relatively soft. An additional relative uncertainty of 0.3 % is assigned to account for the difference in the hadronic interaction probabilities for kaons and pions between data and simulation. A further uncertainty arises from the modelling of the hardware trigger in the simulation. This is estimated using a data-driven technique and leads to a relative systematic uncertainty of 1.1 % on the ratio of branching fractions. Variations in the procedure used to determine the relative particle identification efficiency lead to a relative uncertainty of 0.3 %. Possible systematic effects on the efficiency for B0 s→φφ due to the finite width difference in the B0 ssystem [44] have been checked, and found to be negligible. The value of B(φ→K+K−) is taken from ref. [7] and contributes a relative uncertainty of 1.0 %. The value of B(K∗(892)0→K+π−) is taken to be 2/3 exactly. The systematic uncertainties are summarized in table 2. Summing these in quadrature gives a relative uncertainty of 3.8 % on the ratio of branching fractions. The knowledge of the fragmentation fraction ratio, fs/fd, gives a relative uncertainty of 5.8 %, which is quoted separately. The ratio of branching fractions is found to be B(B0 s→φφ) B(B0→φK∗)= 1.84 ±0.05 (stat) ±0.07 (syst) ±0.11 (fs/fd). – 7 – JHEP10(2015)053 ] 2 c) [MeV/ − K + K − K + K(m 5200 5300 5400 5500 5600 ) 2 cCandidates / (9.0 MeV/ -2 10 -1 10 1 10 2 10 3 10 LHCb Figure 3. The K+K−K+K−invariant mass with the tight BDT selection applied. A fit to the total PDF as described in the text is shown as a (red) solid line, B0 s→φφ as a (blue) long-dashed line, B0→φφ as a (blue) short-dashed line, and the combinatorial background as a (purple) dotted line. This is converted into an absolute branching fraction using B(B0→φK∗(892)0) = (1.00 ± 0.04±0.05)×10−5, which is obtained by averaging the results in refs. [45] and [46] assuming that the uncertainties due to the fragmentation fractions and S-waves are fully correlated between the two measurements. The resulting value for the absolute branching fraction is B(B0 s→φφ) = (1.84 ±0.05 (stat) ±0.07 (syst) ±0.11 (fs/fd)±0.12 (norm)) ×10−5. 6 Search for the decay B0→φφ To search for the B0→φφ decay mode, the tight BDT selection described in section 3is used. To fit for a putative B0→φφ signal, the same signal model as for the B0 ssignal is used. The mean value of the signal mass is shifted relative to the B0 smode by the known B0 s–B0mass splitting, and the resolution parameters are kept common between the two modes. The resulting fit is shown in figure 3. The data are consistent with having no B0→φφ contribution. The fitted B0signal has a yield of 5 ±6 events, and the statistical significance is less than 2 standard deviations, hence an upper limit is placed on the branching fraction of the decay. To determine this limit, a modified frequentist approach, the CLsmethod, is used [47]. The method provides CLs+b, a measure of the compatibility of the observed distribution with the signal plus background hypothesis, CLb, a measure of the compatibility with the background only hypothesis, and CLs= CLs+b/CLb. The expected and observed CLs values as a function of the branching fraction are shown in figure 4. This gives, at 90% confidence level, an upper limit of B(B0→φφ)<2.8×10−8. At 95% confidence level, the upper limit is found to be B(B0→φφ)<3.4×10−8. – 8 – JHEP10(2015)053 B. Sciascia18, A. Sciubba25,l, A. Semennikov31, N. Serra40, J. Serrano6, L. Sestini22, P. Seyfert20, M. Shapkin35, I. Shapoval16,43,f , Y. Shcheglov30, T. Shears52, L. Shekhtman34, V. Shevchenko64, A. Shires9, B.G. Siddi16, R. Silva Coutinho48, L. Silva de Oliveira2, G. Simi22, M. Sirendi47, N. Skidmore46, I. Skillicorn51, T. Skwarnicki59, E. Smith55,49, E. Smith53, I.T. Smith50, J. Smith47, M. Smith54, H. Snoek41, M.D. Sokoloff57,38, F.J.P. Soler51, F. Soomro39, D. Souza46, B. Souza De Paula2, B. Spaan9, P. Spradlin51, S. Sridharan38, F. Stagni38, M. Stahl11, S. Stahl38, S. Stefkova53, O. Steinkamp40, O. Stenyakin35, S. Stevenson55, S. Stoica29, S. Stone59, B. Storaci40, S. Stracka23,s, M. Straticiuc29, U. Straumann40, L. Sun57, W. Sutcliffe53, K. Swientek27, S. Swientek9, V. Syropoulos42, M. Szczekowski28, P. Szczypka39,38, T. Szumlak27, S. T’Jampens4, A. Tayduganov6, T. Tekampe9, M. Teklishyn7, G. Tellarini16,f , F. Teubert38, C. Thomas55, E. Thomas38, J. van Tilburg41, V. Tisserand4, M. Tobin39, J. Todd57, S. Tolk42, L. Tomassetti16,f , D. Tonelli38, S. Topp-Joergensen55, N. Torr55, E. Tournefier4, S. Tourneur39, K. Trabelsi39, M.T. Tran39, M. Tresch40, A. Trisovic38, A. Tsaregorodtsev6, P. Tsopelas41, N. Tuning41,38, A. Ukleja28, A. Ustyuzhanin65,64, U. Uwer11, C. Vacca15,e, V. Vagnoni14, G. Valenti14, A. Vallier7, R. Vazquez Gomez18, P. Vazquez Regueiro37, C. V´azquez Sierra37, S. Vecchi16, J.J. Velthuis46, M. Veltri17,g, G. Veneziano39, M. Vesterinen11, B. Viaud7, D. Vieira2, M. Vieites Diaz37, X. Vilasis-Cardona36,o, A. Vollhardt40, D. Volyanskyy10, D. Voong46, A. Vorobyev30, V. Vorobyev34, C. Voß63, J.A. de Vries41, R. Waldi63, C. Wallace48, R. Wallace12, J. Walsh23, S. Wandernoth11, J. Wang59, D.R. Ward47, N.K. Watson45, D. Websdale53, A. Weiden40, M. Whitehead48, G. Wilkinson55,38, M. Wilkinson59, M. Williams38, M.P. Williams45, M. Williams56, T. Williams45, F.F. Wilson49, J. Wimberley58, J. Wishahi9, W. Wislicki28, M. Witek26, G. Wormser7, S.A. Wotton47, S. Wright47, K. Wyllie38, Y. Xie61, Z. Xu39, Z. Yang3, J. Yu61, X. Yuan34, O. Yushchenko35, M. Zangoli14, M. Zavertyaev10,b, L. Zhang3, Y. Zhang3, A. Zhelezov11, A. Zhokhov31, L. Zhong3, S. Zucchelli14 1Centro Brasileiro de Pesquisas F´ısicas (CBPF), Rio de Janeiro, Brazil 2Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil 3Center for High Energy Physics, Tsinghua University, Beijing, China 4LAPP, Universit´e Savoie Mont-Blanc, CNRS/IN2P3, Annecy-Le-Vieux, France 5Clermont Universit´e, Universit´e Blaise Pascal, CNRS/IN2P3, LPC, Clermont-Ferrand, France 6CPPM, Aix-Marseille Universit´e, CNRS/IN2P3, Marseille, France 7LAL, Universit´e Paris-Sud, CNRS/IN2P3, Orsay, France 8LPNHE, Universit´e Pierre et Marie Curie, Universit´e Paris Diderot, CNRS/IN2P3, Paris, France 9Fakult¨at Physik, Technische Universit¨at Dortmund, Dortmund, Germany 10 Max-Planck-Institut f¨ur Kernphysik (MPIK), Heidelberg, Germany 11 Physikalisches Institut, Ruprecht-Karls-Universit¨at Heidelberg, Heidelberg, Germany 12 School of Physics, University College Dublin, Dublin, Ireland 13 Sezione INFN di Bari, Bari, Italy 14 Sezione INFN di Bologna, Bologna, Italy 15 Sezione INFN di Cagliari, Cagliari, Italy 16 Sezione INFN di Ferrara, Ferrara, Italy 17 Sezione INFN di Firenze, Firenze, Italy 18 Laboratori Nazionali dell’INFN di Frascati, Frascati, Italy 19 Sezione INFN di Genova, Genova, Italy 20 Sezione INFN di Milano Bicocca, Milano, Italy 21 Sezione INFN di Milano, Milano, Italy 22 Sezione INFN di Padova, Padova, Italy 23 Sezione INFN di Pisa, Pisa, Italy 24 Sezione INFN di Roma Tor Vergata, Roma, Italy 25 Sezione INFN di Roma La Sapienza, Roma, Italy – 15 – JHEP10(2015)053 26 Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences, Krak´ow, Poland 27 AGH - University of Science and Technology, Faculty of Physics and Applied Computer Science, Krak´ow, Poland 28 National Center for Nuclear Research (NCBJ), Warsaw, Poland 29 Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest-Magurele, Romania 30 Petersburg Nuclear Physics Institute (PNPI), Gatchina, Russia 31 Institute of Theoretical and Experimental Physics (ITEP), Moscow, Russia 32 Institute of Nuclear Physics, Moscow State University (SINP MSU), Moscow, Russia 33 Institute for Nuclear Research of the Russian Academy of Sciences (INR RAN), Moscow, Russia 34 Budker Institute of Nuclear Physics (SB RAS) and Novosibirsk State University, Novosibirsk, Russia 35 Institute for High Energy Physics (IHEP), Protvino, Russia 36 Universitat de Barcelona, Barcelona, Spain 37 Universidad de Santiago de Compostela, Santiago de Compostela, Spain 38 European Organization for Nuclear Research (CERN), Geneva, Switzerland 39 Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Lausanne, Switzerland 40 Physik-Institut, Universit¨at Z¨urich, Z¨urich, Switzerland 41 Nikhef National Institute for Subatomic Physics, Amsterdam, The Netherlands 42 Nikhef National Institute for Subatomic Physics and VU University Amsterdam, Amsterdam, The Netherlands 43 NSC Kharkiv Institute of Physics and Technology (NSC KIPT), Kharkiv, Ukraine 44 Institute for Nuclear Research of the National Academy of Sciences (KINR), Kyiv, Ukraine 45 University of Birmingham, Birmingham, United Kingdom 46 H.H. Wills Physics Laboratory, University of Bristol, Bristol, United Kingdom 47 Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom 48 Department of Physics, University of Warwick, Coventry, United Kingdom 49 STFC Rutherford Appleton Laboratory, Didcot, United Kingdom 50 School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom 51 School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom 52 Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom 53 Imperial College London, London, United Kingdom 54 School of Physics and Astronomy, University of Manchester, Manchester, United Kingdom 55 Department of Physics, University of Oxford, Oxford, United Kingdom 56 Massachusetts Institute of Technology, Cambridge, MA, United States 57 University of Cincinnati, Cincinnati, OH, United States 58 University of Maryland, College Park, MD, United States 59 Syracuse University, Syracuse, NY, United States 60 Pontif´ıcia Universidade Cat´olica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil, associated to2 61 Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China, associated to3 62 Departamento de Fisica , Universidad Nacional de Colombia, Bogota, Colombia, associated to8 63 Institut f¨ur Physik, Universit¨at Rostock, Rostock, Germany, associated to11 64 National Research Centre Kurchatov Institute, Moscow, Russia, associated to 31 65 Yandex School of Data Analysis, Moscow, Russia, associated to31 66 Instituto de Fisica Corpuscular (IFIC), Universitat de Valencia-CSIC, Valencia, Spain, associated to36 67 Van Swinderen Institute, University of Groningen, Groningen, The Netherlands, associated to41 aUniversidade Federal do Triˆangulo Mineiro (UFTM), Uberaba-MG, Brazil bP.N. Lebedev Physical Institute, Russian Academy of Science (LPI RAS), Moscow, Russia – 16 – JHEP10(2015)053 cUniversit`a di Bari, Bari, Italy dUniversit`a di Bologna, Bologna, Italy eUniversit`a di Cagliari, Cagliari, Italy fUniversit`a di Ferrara, Ferrara, Italy gUniversit`a di Urbino, Urbino, Italy hUniversit`a di Modena e Reggio Emilia, Modena, Italy iUniversit`a di Genova, Genova, Italy jUniversit`a di Milano Bicocca, Milano, Italy kUniversit`a di Roma Tor Vergata, Roma, Italy lUniversit`a di Roma La Sapienza, Roma, Italy mUniversit`a della Basilicata, Potenza, Italy nAGH - University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunications, Krak´ow, Poland oLIFAELS, La Salle, Universitat Ramon Llull, Barcelona, Spain pHanoi University of Science, Hanoi, Viet Nam qUniversit`a di Padova, Padova, Italy rUniversit`a di Pisa, Pisa, Italy sScuola Normale Superiore, Pisa, Italy tUniversit`a degli Studi di Milano, Milano, Italy †Deceased – 17 –